Implicit leader election in a distributed storage network

ABSTRACT

A method begins by a processing module receiving a request to store a data object in distributed storage (DS) units. The processing module generates and transmits a proposal message that includes a preferred source name, and a proposal attempt identifier to a plurality of DS units. The processing module then receives a proposal message acceptance response from at least one of the plurality of DS units and when the proposal message response indicates that no other proposal messages have been received by at least one of the plurality of DS units, retains the preferred source name included within the proposal message as a persistent value for the source name.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an example of overlapping writerequests for a set of encoded data slices in accordance with the presentinvention;

FIG. 10 is a schematic block diagram of an example of a write requestfor an encoded data slice in accordance with the present invention;

FIG. 11 is a schematic block diagram of an example of a read request foran encoded data slice in accordance with the present invention;

FIG. 12 is a schematic block diagram of another example of overlappingwrite requests and read requests for a set of encoded data slices inaccordance with the present invention;

FIG. 13 is a schematic block diagram of an example of proposal recordsfor a set of encoded data slices stored by storage units of the DSN inaccordance with the present invention;

FIG. 14 is a schematic block diagram of an example of a proposal messagefor an in accordance with the present invention; and

FIG. 15 is a logic diagram of an example of a method of using a proposalmessage to manage contention in the DSN in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generate aper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate a per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an example of overlapping writerequests for a set of encoded data slices having the same set of slicenames. Overlapping write requests occur when one set of write requestsis pending (e.g., write finalize commands have not yet been issued) andanother set of write requests for a set of encoded data slices havingthe same set of slice names is received by the storage units. In thisexample, computing devices A and B send overlapping write requestsregarding a set of encoded data slices with the same set of slicesnames.

To process overlapping write requests (and other overlapping data accessrequests), each storage unit 36 (SU#1-SU#5) stores its own proposalrecord 90-1 through 90-5 for a slice name or for a group of slice names.A proposal record 90 includes an order listed of pending transactions 92and an ordered list of visible and different versions of an encoded dataslice (EDS) 94 have the same slice name. The proposal record 90 mayfurther include an indication of the current revision level of theencoded data slice.

The ordered list of pending transactions 92 include a time ordered listof transaction numbers, or other indication, associated with data accessrequests regarding the slice name that were received while the proposalrecord is open (e.g., write finalize commands have not yet been issuedfor one of the pending write requests). For example, the proposal record90-1 of storage unit #1 includes an ordered list of transaction numbersfor data access requests regarding a first slice name of a set of slicenames.

As a specific example, a first write request from computing device Aregarding a version of an encoded data slice having the first slice namehas a first transaction number (e.g., 0413) and a second write requestfrom computing device B regarding another version of the encoded dataslice having the first slice name has a second transaction number (e.g.,0279). Storage unit #1 received the first write request before receivingthe second write request, as such the proposal record 90-1 has the firstwrite request (e.g., the first transaction number) in a first priorityposition and the second write request in a second priority position.

As another specific example, a write request from computing device Aregarding a version of an encoded data slice having a second slice namehas the first transaction number (e.g., 0413) and a write request fromcomputing device B regarding another version of the encoded data slicehaving the second slice name has the second transaction number (e.g.,0279). Storage unit #2 received the write request from computing deviceB before receiving the write request from computing device A. As such,the proposal record 90-2 has the write request of computing device B(e.g., the second transaction number) in the first priority position andthe write request from computing device A in a second priority position.The remaining storage units generate their respective proposal recordsin a similar manner.

In general, a storage unit “opens” a proposal record when it receives anew write request for a version of an encoded data slice having a slicename (i.e., no other write requests are pending). The storage unit sendsthe proposal record to the computing device sending the write request.If there are no overlapping write requests for a set of encoded dataslices having a set of slice names, then the other storage units (SU#2-SU#5) open up proposal records and send them to the computing device.

The computing device interprets the proposal records to determinewhether a threshold number, or more, (e.g., decode threshold number,write threshold number, etc.) of its write requests is in the firstpriority position. When there is not an overlapping write request, thewrite requests will be in the first priority position. As such, thecomputing device sends finalize requests to the storage units. Thestorage units process the finalize request to make the new version ofthe encoded data slices as the most recent set of encoded data slicesand close their respective proposal records.

When there is an overlapping write request (e.g., a storage unit has anopen proposal record for the slice name), the storage unit updates theproposal record with the new write request by placing the new writerequest is a lower priority position than previously received andpending write requests. After updating the proposal record, the storageunit sends the proposal record to the computing device that sent the newwrite request.

As the computing devices receive the proposal records, it determineswhether at least the threshold number of their respective write requestsare in first priority position. If yes, the computing device issues thefinalize commands. If not, the computing device withdraws it writerequests or executes some other fallback position.

In addition to the two write requests, computing device C is sendingread requests to the storage units for the set of encoded data slices98. The storage units add the read requests to their respective proposalrecords and send the updated proposal records to computing device C.Upon receiving the proposal records, computing device C determineswhether to proceed with the read request (e.g., read the currentrevision level of the set of encoded data slices) or terminate the readrequest. As an alternative, computing device C processes the proposalrecords to determine that the new set of encoded data slices fromcomputing device A or computing device B will be the next currentversion of the set of encoded data slices. Having made thisdetermination, computing device C modifies its read requests to read thenext current version of the set of encoded data slices.

FIG. 10 is a schematic block diagram of an example of a write request 96or 100 of FIG. 9. The write request includes a transaction number field,a slice name (SN) field, an encoded data slice (EDS) field, a currentrevision level field, and a new revision level field. Each write requestin the set of write requests includes the same transaction number, adifferent slice name, a different EDS, the same current revision level,and the same new revision level.

FIG. 11 is a schematic block diagram of an example of a read request 102of FIG. 9. The read request includes a transaction number field, a slicename (SN) field, and a current revision level field. Each read requestin the set of read requests 102 includes the same transaction number, adifferent slice name, and the same current revision level.

FIG. 12 is a schematic block diagram of another example of overlappingwrite requests 96, 100 and read requests 102 for a set of encoded dataslices 98. In this example, each of computing devices A and B encodeddata segment into a set of five encoded data slices. Accordingly, eachof computing devices A and B generates five write requests 96-1 through96-5 and 100-1 through 100-5. The write requests from computing device Ainclude the same transaction number of 0413 (which may be randomlygenerated, may be a time stamp, etc.), differing slice names (SN 1_1through SN 5_1), differing encoded data slices (EDS A_1_1 through EDSA_5_1), the same current revision level of 003, and the next revisionlevel of 004.

The write requests form computing device B include the same transactionnumber of 0279, differing slice names (SN 1_1 through SN 5_1), differingencoded data slices (EDS B_1_ 1 through EDS B_5_1), the same currentrevision level of 003, and the next revision level of 004. A comparisonof the write requests from computing device A with the write requestsfrom computing device B yields that the write requests have the sameslice names, the same current revision levels, and the same nextrevision levels. The write requests differ in the transaction numbersand in the encoded data slices.

Computing device C issues five read requests for the set of encoded dataslices 98. The read requests 102-1 through 102-5 includes the sametransaction number of 03338, different slice names (SN 1_1 through SN5_1), and the current revision level of 003. The write requests and theread requests are sent to the storage units SU1 through SU5, whichprocesses the requests as discussed with reference to FIG. 13.

FIG. 13 is a schematic block diagram of an example of proposal recordsfor a set of encoded data slices stored by storage units of the DSN. Inthis example, while the write requests 96 and 100 and the read requests102 are sent out at similar times, due to differing latencies and/orprocessing capabilities between the computing devices and storage units,the requests are received at different times and, potentially in adifferent order, by the storage units than the order in which they weretransmitted.

Prior to the reception of any of the read or write requests, the storageunits store a current revision level of the set of encoded data slices.As shown, storage unit SU#1 stores EDS 1_1, storage unit SU#2 stores EDS2_1, and so on. In this example, the current revision level of theencoded data slices is 003. In addition, each of the storage units donot have a proposal record open for their respective encoded data slice.

In this example, when a storage unit receives a data access request, itopens a proposal record that identifies the data access it justreceived, the current revision level, and an indication that the currentrevision level of the encoded data slice is visible (e.g., can beaccessed by a computing device of the DSN). Upon opening a proposalrecord, the storage unit sends it to the computing device from which itreceived the request.

For example, each of storage units 1, 3, 4, and 5 received the writerequest from computing device A first. Accordingly, each storage unitcreates a proposal record that includes the ordered list of pendingtransactions 92 and the order list of visible different versions of EDS94, which is sent to computing device A. For instance, each of theordered list of pending transactions 92-1, 92-3, 92-4, and 92-5 includethe transaction number of 0413 (the transaction number for the writerequests of computing device A) in the first priority position. Further,each of the order list of visible different versions of EDS 94-1, 94-3,94-4, and 94-5 includes an indication that the current revision level ofthe encoded data slice and the encoded data slice from computing deviceA are visible (e.g., for SU #1, EDS 1_1 and EDS A_1_1 are visible).

Continuing with the example, storage unit #2 receives the write requestfrom computing device B first. Accordingly, storage unit #2 creates aproposal record that includes the ordered list of pending transactions92-2 and the order list of visible different versions of EDS 94-4, whichis sent to computing device B. For instance, the ordered list of pendingtransactions 92-2 includes the transaction number of 0279 (thetransaction number for the write requests of computing device B) in thefirst priority position. Further, the order list of visible differentversions of EDS 94-2 includes an indication that the current revisionlevel of the encoded data slice and the encoded data slice fromcomputing device B are visible (e.g., EDS 2_1 and EDS B_2_1 arevisible).

After receiving the write requests from computing device A, storageunits 1, 3, 4, and 5 receive the write request from computing device B.Accordingly, each storage unit updates its proposal record, which aresent to computing device A. For instance, each of the ordered list ofpending transactions 92-1, 92-3, 92-4, and 92-5 are updated to includethe transaction number of 0279 (the transaction number for the writerequests of computing device B) in the second priority position.Further, each of the order list of visible different versions of EDS94-1, 94-3, 94-4, and 94-5 are updated to include an indication that thecurrent revision level of the encoded data slice and the encoded dataslices from computing devices A and B are visible (e.g., for SU #1, EDS1_1 EDS A_1_1, and EDS B_1_1 are visible).

After receiving the write requests from computing device B, storage unit2 receives the write request from computing device A. Accordingly,storage unit #2 updates its proposal record, which is sent to computingdevice B. For instance, the ordered list of pending transactions 92-2includes the transaction number of 0413 (the transaction number for thewrite requests of computing device A) in the second priority position.Further, the order list of visible different versions of EDS 94-2includes an indication that the current revision level of the encodeddata slice and the encoded data slices from computing devices A and Bare visible (e.g., EDS 2_1, EDS B_2_1, and EDS A_2_1 are visible).

After receiving the write requests from both computing devices A and Band prior to closing the proposal records, the storage units receiveread requests from computing device C. Accordingly, each of the storageunits updates its proposal record to include the read request. Inparticular, each storage unit updates its order list of pendingtransaction 92 to include the transaction number 0279 of the readrequests in the third priority position. The update proposal records aresent to computing device C.

Updates to a plurality of encoded data slices can be achieved as part ofa single transaction. These operations, often described as “atomicoperations” can be executed without any other process being able to reador change state (other than the read or change being updated) during theoperation. Atomic operations are effectively executed as a single step,and can be an important attribute in systems where a plurality ofalgorithms deal with multiple independent processes (for algorithms thatupdate shared data without requiring synchronization and those that doneed synchronization). A normal atomic write operation can be executedas follows: 1) a DS processor acquires a lock on the metadata associatedwith a data object; 2) the DS processing unit writes the object intomemory; 3) the DS processing unit updates the metadata associated withthe data object with a quota usage for the container in which the dataobject is written; 4) if the quota update to the metadata associatedwith the data object is unsuccessful the data object is deleted frommemory; and 5) the DS processing unit releases the lock on the metadataassociated with the data object.

In this instance, the atomic operation enforces that all updates beingmade in a current transaction are immediately consistent; if anyfailures occur during the update the entire transaction is aborted andany objects already written are rolled back. Atomic operations, such asthat described above, can result in contention for metadata associatedwith a data object if there are multiple writes in the same container,in addition to inefficiency related to deletion of an already writtendata object because the associated metadata update was unsuccessful.

The DSN protocol enables single-element atomic “compare and swap of 1element” (CAS-1), where a data source may be atomically incremented inan atomic transactional operation. Supporting multiple (arbitrary N)atomic compare and swap (CAS-N) transactions requires extensions to theDSN protocol, which are accomplished by using the protocol to enableN-Element compare and swap (CAS-N). CAS-N provides strong consistencyamong arbitrary elements with atomic read visibility. A CAS-Ntransaction consists of a set of “proposals” which take the form(SOURCE, OLD REVISION, NEW REVISION), where each proposal's revisioncomparisons must be atomically satisfied in order for the transaction tocomplete, and if any fail no update to any source is made. This isaccomplished by storing the complete “transaction description”—(the setof all proposals) in each data location (for example, on eachparticipating ds unit holding a slice of at least one of the sourceswithin the CAS-N operation) at least for the duration the transaction.Any reader or writer that encounters a source with an ongoing (open)transaction must then validate the entire transaction's proposals beforedeciding whether the NEW REVISION or OLD REVISION is visible for thatsource. In some cases, this may require a client to issue additionalread requests for previously unknown sources that are referenced withinthe transaction description. When a CAS-N transaction is complete, thetransaction description can be removed from all participating ds units,and at such time the NEW REVISION of each source is visible.

When more than one proposer attempts to update the same revision/versionof a data source at the same time, contention arises. The proposers inthis case are generally two or more DS processing units attempting toupdate the same data source (segment or data source representing themeta-data object) at the same time. Since strong consistency is adesirable property for a distributed storage system, some form of theconsensus protocol is generally required. Typically, if all actors inthe system follow such a protocol, strong consistency is insured inaddition, it is also advantageous for consensus to be achievable in asingle round-trip in the contented case while performing acceptably inthe case where multiple writers are attempting to update the same dataobject (i.e., “the contended case”).

Protocols such as Paxos were developed to establish a distinguishedclient using an out-of-band process to deal with consensus issues. Agoal of Paxos is for some number of peers to reach an agreement on avalue; Paxos guarantees that if one peer believes some value has beenagreed upon by a majority the majority will never agree on a differentvalue. The protocol is designed such that any agreement must go througha majority of nodes. The out-of-band process of Paxos may add overheadto the distributed storage system because round trips may be required.Strong consistency properties are achieved during an overwrite of aspecific revision or the initial right of some sort's name in DSN memory(sometimes called “a contest”). Multiple DS processing units participatein the same contest if they attempt to update the same revision of adata source stored in the same DSN memory.

A variant of Paxos, called “Fast Paxos” was developed to enableconsensus to be established in a single network round trip time (RTT) inthe ideal case of no contention. Fast Paxos generally has separatedistinct phases for: 1) electing a leader; and 2) proposing new valuesfor consensus. A proposal for a Fast Paxos variant that combines theproposal with leader election phases is shown below. This is done byusing proposal metadata as ballots in a leader election. This Fast Paxosvariant we call “Fast Paxos with Implicit Leader Election” (FPILE).FPILE enables single RTT writes in the best case, along with guaranteedstrong consistency and fast contention resolution (typically 2 RTTs).

FPILE is designed to achieve the following goals: 1) Safety (Strongconsistency (readers can only see accepted values); 2) Availability(Deadlock-free (progress can always be made)); and, 3) Performance (FastContention Resolution (in a single round trip in the average case)).

Typical data protocols are subject to a variety of fault conditions.While these protocols may offer strong consistency, they do not provideavailability or performance as described here. Under previously existingprotocols, client crashes and store outages may leave the system in anindeterminate state, where no forward progress can be made since thelatest restorable revision cannot be determined. These protocols mayalso suffer from poor contention resolution, in that clients may keepretrying their requests with no coordinated back-off mechanism asidefrom exponential back-off. This leads to long delays in the event thatcontention is encountered.

The goals of FPILE include (but are not limited to) maintaining strongconsistency guarantees of existing protocols, while also enhancingperformance and availability. These properties are discussed in moredetail below.

As discussed above, there are multiple positive improvements that CAS-Nwill provide to the distributed storage network, but some of thebenefits include a strong guarantee that either the old or the newrevision of an object is visible (such as in the case of a Slicestor(IBM server) power outage (such as a power outage beyond W-T SS). Thismeans concurrent structures like the index are always guaranteed to becorruption free during a power loss. In the case of a power outage,there are no “partial writes”—all writes are atomic (as seen in FIG.13). This means that the index, metadata and usage will always be insync even after client crashes.

Writes can be acknowledged to a user in a single round trip for the casewhere no contention exists. This improves performance in a WANenvironment by reducing write latency by a factor of 2. Contentionoverhead is also reduced in the case of multiple writes updating thesame node, by allowing heavily contended objects (like sequential writesto an index without delegation) to be completed in a more predictableand lower latency way. Using intents—index and other “derived”structures can be updated asynchronously—this also reduces number ofround trips to achieve write success.

Safety, properties of the existing and newly proposed protocols may bedescribed as a form of “strong, immediate, consistency”, this embodies ahost of guarantees: PFILE includes at least these properties in itsconcept of safety.

-   -   1) readers can only return values that were successfully written        i.e. “accepted” by the system (non-triviality) There can be at        most one accepted value, where an accepted value is a proposed        successor for a given revision (safety);    -   2) all clients can that can make a definitive conclusive        agreement on what the accepted value is (consistency);    -   3) a read initiated after a successful write will always see        that write or a newer one (immediacy a.k.a. linearizability);    -   4) Compare and Swap operations will either entirely fail or        entirely succeed (atomicity).

The following sections outline the protocol and algorithm from theperspective of independently operating clients:

Compare and Swap Algorithm (Write/Update/Delete) Writer: A writer writesa txid, revision, data to each store. The store respects the samecontract and returns the same ballot outlined in fast-casn, except thestore also includes which rounds are active for every table in theballot write(source_name, data, proposed_revision, txid, current_round,stores[ ], to_rebuild, current_revision): for i in stores.size: //Optimization: only send slice data if (current_round == 0) (or whenrebuilding someone else) slice = current_round == 0 ∥ i in to_rebuild ?get_slice(data, i) : null ballots[i] = write(current_revision,current_round, store[i], (txid, proposed_revision, slice)) to all Wstores. // Determine how many ballots contain txid as the first placetxid for the current round success_count = count_my_successes(ballots,current_round) if success_count >= Threshold.WRITE: // We won forupdating the current_revision finalize( ) return Operation.SUCCESS; elseif (count_max_successes(ballots, current_round) >= Threshold.WRITE) orcontains_finalized_slice(ballots): // Someone else beat us for thisupdate, tell the stores its safe to remove our slices withdraw_writes( )// Optimization: May provide hint of what the new data or revision is ifit can be determined from ballots return Operation.RETRIABLE_FAILUREelse: // Neither we, nor anyone else has definitevely won for thisupdate (there is contention) // First determine a set of transactionsthat cannot definitively be ruled out as having at least Threshold.WRITEsuccesses potential_winners = potentially_at_wt(ballots, current_round)if potential_winners.size( ) == 0: // No one could have reached writethreshold in this round, we need to determine a leader to proceedexcluded_candidates = [ ] // No one is rejected yet leader =sloppy_leader_election(ballots, current_round, excluded_candidates)while leader is all inactive and below Threshold.IDA:excluded_leaders.append(leader) leader = sloppy_leader_election(ballots,current_round, excluded_leaders) // We have an active leader, bydefinition (if no else is, we are still active) else ifpotential_winners.size( ) == 1: leader = potential_winner[0] // thispotential winner would also have won the leader election else ifpotential_winners.size( ) > 1: // We can't make any of these the winner,because they may have won on the failed stores // this could only happenif f > min(W − WT, WT − T) return Operation.FAILURE; if leader == txid:// We are the leader, enter the next round write(data,proposed_revision, txid, (current_round + 1), stores[ ], { },current_revision) else: // Someone else is the leader leader_successes =count_leader_successes(ballots, current_round, leader) ifis_inactive(leader): if (leader_successes < Threshold.IDA):withdraw_writes( ) // a potential winner is below T (unrebuildable) //this could only happen if f > min(W − WT, WT − T) returnOperation.FAILURE; else: withdraw_writes( ) if (leader_successes <Threshold.WRITE): // Rebuild the leader's data, they are aboveThreshold.IDA but below Threshold.WRITE // get stores that don't haveleader to_rebuild = {i for i in ballots.size if nothas_leader-revision(ballots[i])} else: // don't need to rebuild anythingto_rebuild = { } write(leader-data, leader-revision, leader-txid,leader_round + 1, stores[ ], to_rebuild, current_revision) else: //leader is active withdraw_writes( ) write(data, proposed_revision, txid,current_round /* not current_round + 1 !!!*/, stores[ ], { },current_revision) potentially_at_wt(ballots, current_round): return allcandidates whose first place votes + num_failures >= WT /*** * Returns asingle candidate leader who can proceed to the next round (if there arepartitions some clients may disagree on who the leader is) */sloppy_leader_election(ballots, current_round, excluded_leaders):valid_candidates = remove_excluded_leaders(ballots, current_round,excluded_leaders) m = get_max_first_places_in_round(ballots,current_round, valid_candidates) potential_leaders = {c invalid_candidates(ballots) | num_first_places(c) == m} returnpotential_leader having the highest txid Read Reader: Very similar towriter: read( ): ballots = reader initially can just read T slicestorsif are the T slices of a revision with at least one finalized slice:return that revision else: read more until there is a source that hasachieved WT, or it is impossible for any source to achieve WT, thenrerun if there is a revision that achieves WT: return it if it is atleast WT inactive, simply retry otherwise else: if failures arewildcards and it is impossible for any revision to have achieved WT: canfallback to previous revision else: leader =sloppy_leader_election(ballots, current_round) if all_inactive(leader)and the leader is the only possible achiever of WT (with failures) andthe leader is above T: rebuild it to WT else: retry Algorithm PropertiesLet f be the number of failed stores Define writer progress as success,failure with a competitor winning, or retrying that would succeed if acompetitor cooperated Claim 1: Writer can always make progress as longas f <= min(WT − T, W − WT) A writer clearly can't succeed if there areless than WT stores available, so f <= W − WT. Let A be a competingwriter, and a be the number of first place votes A has achieved. If a +f < WT, then we can safely open a new round as described in thealgorithm. Else a + f >= WT. If a >= T, we can simply restore thatrevision, and fail our write. Suppose then a = T − 1. We would like itso that it is impossible for A to achieve WT. That means we need X ‘notA’s such that: (W − (T − 1)) − x < WT − (T − 1) (The stores that haven'twon on A − X needs to be less that the amount of remaining wins Arequires to achieve WT) => x > W − WT Then, the least number of ‘not A’srequired is x = W − WT + 1 Then the largest f can be is f <= (W − (T −1)) − X (num stores that haven't already accepted A − num ‘not A’srequired) f <= (W − (T − 1)) − (W − WT + 1) f <= WT − T s >= 3f + 1 (orW >= 4f + 1)

FIG. 14 is a schematic block diagram of an example of a proposal messagefrom a processing module of the DSN. The write request includes atransaction number field, a slice name (SN) field that it would like toupdate, an encoded data slice (EDS) field that the processing module isrequesting to be the persistent value, and a proposal identifier thatmay be incremented according to the proposal attempt being representedby the proposal attempt.

As shown in the logic diagram of FIG. 15, a processing module receives arequest to store a data object in step 502 and generates a proposalmessage (write request) for at least a threshold number of distributedstorage (DS) units in step 504. The method continues in step 504, withprocessing module transmitting proposal message to the threshold numberof DS units. When a DS unit (of the threshold number of DS units)receives the proposal message for a source name for which it has notreceived another proposal, or if all previous requests for the sourcename had a lower round, the DS unit accepts the request and returns aresponse to the processing module indicating the value it has mostrecently accepted, as well as an indication of any other proposals ithas seen. If the ds processing module determines that the value acceptedfrom a write threshold of ds units is for the highest round involved, itmay consider consensus achieved for that value and assume normalconsistency guarantees. At this point, the ds processing unit notifiesthe requester that the proposed value has been successfully accepted bythe threshold number of DS storage units and that value becomes thepersistent value for the subject source name.

If a write threshold number of DS units have not achieved consensus(i.e. the same proposed message has been accepted and a responsereturned for a write threshold number of DS units) the processing modulewill then examine the proposal message responses to determine whetherany other proposal messages have been received and accepted for thesubject source name. When proposal messages have been received andaccepted for the subject source name by the threshold number of DSunits, the processing module along with third party processing modulesthat have transmitted proposal messages for the subject source name areable to determine whether any of the proposal messages accepted by theDS units might be able to achieve consensus for the source name value.When a consensus value is determined to be available, that value maythen be re-proposed (i.e., the proposal message for that value may betransmitted again to the threshold number of DS units) and theprocessing module that generated that proposal message is determined tobe the leader.

Only the leader may continue by re-proposing the value with anincremented round, all other processing units that transmitted proposalmessages for the subject source name may choose to notify theirrequesters of failure at this point (i.e. that they lost the contest).All other processing units may, in addition to notifying the requesters,may provide the persistent value, depending on the networkefficiency/load, in order to reduce round trips.

In some cases the processing module (along with all other processingmodules) may determine that there are two values that are potentiallyavailable as consensus values, even though none is a clear leader. Inthis case the processing modules associated with both of the potentialconsensus values may re-propose with their proposal messages in order todetermine a consensus value and leader. This two-round system (alsoknown as the second ballot, runoff voting or ballotage) is used toselect a leader, where no consensus is achieved, all but the twocandidate values the most votes, are eliminated, and a second round ofvoting is held.

As will be understood by one skilled in the art, the DS processing unithas no obligation to propose any specific value for the purposes ofsafety, however, as shown above, certain schemes in selection of a valuecan greatly reduce the number of round trips required to resolve acontended contest. For example, the DS processing unit can consider theresponses to proposals as ballots in an election. Some examples ofschemes to choose a value from these ballots include: picking theproposal with the oldest timestamp, or using a deterministic function ofthe value. In addition, the DS units can choose to rank the proposalsthey have seen and the DS processing unit can use a ranked voting schemesuch as instant runoff voting (see above). Once a value is chosen, theassociated leader is determined and proceeds with an incremented round;third party processing units in the contest may choose to fail at thispoint or may retry their proposal messages themselves with some delay,or update by modifying the now persistent value and the retry.

Various algorithms may be used to determine this delay, includingexponential backoff and/or using information from the DS units toestimate whether the leader is still active. A DS processing unit thatwishes to read the current agreed value for the subject source name mustensure that any value it reads was persisted to a write threshold beforereturning it to the requester. If it can be determined that it wasimpossible for any value to achieve consensus in a current contestedupdate, the reader may fall back to a previous value of the source name.As an additional optimization, the writer may send a ‘finalize’ requestfor a source name and value after achieving consensus, to inform the dsunits that has consensus has been achieved for the value. Subsequentprocessing modules transmitting read requests for the subject sourcename will not need to do additional work to determine that a writethreshold was achieved for a value before returning the value to arequester by virtue of the source having been finalized.

As would be further understood by one skilled in the relevant art, FastPaxos (and as described above) works well with Trimmed Writes. InTrimmed Writes Systems, it is common to configure Width-WT=WT-Threshold,and the finalize phase provides an ideal location to insert a newcleanup message (e.g. a Finalize message with a boolean flag) thatcleans up not only previous slices, but also the slice content of asuccessfully written slice. This flag would only be set “on” at most(W-WT) of the finalization messages sent to the stores. This frees upexcess data storage that might have been utilized by the extra (W-WT)slices.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: generating a proposal message that includesa persistent value for a source name associated with a data object to beupdated, and a first proposal attempt identifier, wherein the firstproposal attempt identifier can be incremented based on a proposalattempt; transmitting the proposal message to a plurality of distributedstorage units (SUs) of the DSN; receiving, in response to the proposalmessage, a proposal message acceptance response from at least one of theplurality of DS units; and in response to a determination that the atleast one of the plurality of DS units has had no other request for thesource name, retaining the persistent value for the source name.
 2. Themethod of claim 1, wherein the persistent value is determined based onexecution of a deterministic function by the processing module.
 3. Themethod of claim 2, wherein the deterministic function is furtherexecuted by other processing units receiving proposal message responsesfor the source name and further wherein executing the deterministicfunction results in another persistent value for the source name.
 4. Themethod of claim 1, further comprising: in response to a determinationthat the at least one of the plurality of DS units has had a previousrequest for the persistent source name, determining whether the previousrequest for the persistent source name indicates a lower round proposal,wherein a lower round proposal has a second proposal attempt identifierlower than the first proposal attempt identifier; and in response to adetermination that the previous request for the persistent source nameindicates a lower round proposal, retaining the persistent value for thesource name.
 5. The method of claim 4, further comprising: in responseto a determination that the previous request for the persistent sourcename does not indicate a lower round proposal, proposing anotherpersistent value for the source name.
 6. The method of claim 1, whereinthe persistent value is determined based on a balloting process executedby the one or more processing modules.
 7. The method of claim 6, whereinthe balloting process includes choosing a ballot value based on atimestamp associated with the proposal message.
 8. The method of claim7, wherein the balloting process results in two potential persistentvalues, and the persistent value is chosen from the two potentialpersistent values after a second processing module and a thirdprocessing modules associated with the two potential persistent values,respectively, re-transmit proposal messages transmitted after theprocessing module receives a threshold number of proposal messages. 9.The method of claim 8, further comprising: determining, by a selectother processing module of the one or more processing modules, whetherto transmit, by the select other processing module of the one or moreprocessing modules, another proposal message associated with the anotherpersistent value for the source name, wherein the determining is basedon the one or more processing modules receiving a threshold number ofproposal messages; and in response to a determination to transmitanother proposal message associated with the another persistent valuefor the source name, transmitting the another proposal message to theplurality of DS units.
 10. The method of claim 9, wherein thedetermining whether to transmit, by the select other processing moduleof the one or more processing modules, another proposal messageassociated with the another persistent value for the source name isfurther based on the proposal message associated with the anotherpersistent value for the source name including a preferred candidatevalue for the source name.
 11. A computing device comprising: aninterface configured to interface and communicate with a dispersed ordistributed storage network (DSN); memory that stores operationalinstructions; a processing module operably coupled to the interface andto the memory, wherein the processing module, when operable within thecomputing device based on the operational instructions, is configuredto: receive, from a requesting computing device and via the DSN, arequest to store a data object within a plurality of distributed storage(DS) units; generate a proposal message that includes a persistent valuefor a source name associated with a data object to be updated, and afirst proposal attempt identifier, wherein the first proposal attemptidentifier can be incremented based on a proposal attempt; transmit theproposal message to a plurality of distributed storage units (SUs) ofthe DSN; receive, in response to the proposal message, a proposalmessage acceptance response from at least one of the plurality of DSunits; and in response to a determination that the at least one of theplurality of DS units has had no other request for the source name,retain the persistent value for the source name.
 12. The computingdevice of claim 11, wherein the persistent value is determined based onexecution of a deterministic function by the processing module.
 13. Thecomputing device of claim 12, wherein the deterministic function isfurther executed by other processing units receiving proposal messageresponses for the source name and further wherein executing thedeterministic function results in another persistent value for thesource name.
 14. The computing device of claim 11, wherein theprocessing module, when operable within the computing device based onthe operational instructions, is further configured to: in response to adetermination that the at least one of the plurality of DS units has hada previous request for the persistent source name, determine whether theprevious request for the persistent source name indicates a lower roundproposal, wherein a lower round proposal has a second proposal attemptidentifier lower than the first proposal attempt identifier; and inresponse to a determination that the previous request for the persistentsource name indicates a lower round proposal, retain the persistentvalue for the source name.
 15. The computing device of claim 14, whereinthe processing module, when operable within the computing device basedon the operational instructions, is configured to: in response to adetermination that the previous request for the persistent source namedoes not indicate a lower round proposal, propose another persistentvalue for the source name.
 16. The computing device of claim 15, whereinthe persistent value is determined based on a balloting process executedby the one or more processing modules.
 17. The computing device of claim16, wherein the balloting process includes choosing a ballot value basedon a timestamp associated with the proposal message.
 18. The computingdevice of claim 11, wherein the processing module, when operable withinthe computing device based on the operational instructions, is furtherconfigured to: determine, by a select other processing module of the oneor more processing modules, whether to transmit, by the select otherprocessing module of the one or more processing modules, anotherproposal message associated with the another persistent value for thesource name, wherein the determination to transmit is at least partiallybased on the one or more processing modules receiving a threshold numberof proposal messages; and in response to a determination to transmitanother proposal message associated with the another persistent valuefor the source name, transmit the another proposal message to theplurality of DS units.
 19. The computing device of claim 18, wherein thedetermination to transmit another proposal message associated with theanother persistent value for the source name is further based on theproposal message associated with the another persistent value for thesource name including a preferred candidate value for the source name.20. A computer program product comprising one or more computer readablestorage media having program instructions collectively stored on the oneor more computer readable storage media, the program instructionsexecutable to: generate a proposal message that includes a persistentvalue for a source name associated with a data object to be updated in adispersed storage network (DSN), and a first proposal attemptidentifier, wherein the first proposal identifier can be incrementedbased on a proposal attempt; transmitting the proposal message to aplurality of distributed storage units (SUs) of the DSN; receiving, inresponse to the proposal message, a proposal message acceptance responsefrom at least one of the plurality of DS units; and in response to adetermination that the at least one of the plurality of DS units has hadno other request for the source name, retaining the persistent value forthe source name.